Publication detail

Towards Online Data Mining System for Enterprises

KUPČÍK, J. HRUŠKA, T.

Original Title

Towards Online Data Mining System for Enterprises

English Title

Towards Online Data Mining System for Enterprises

Type

conference paper

Language

en

Original Abstract

As the amount of generated and stored data in enterprises increases, the significance of fast analyzing of this data rises. This paper introduces data mining system designed for high performance analyses of very large data sets, and presents its principles. The system supports processing of data stored in relational databases and data warehouses as well as processing of data streams, and discovering knowledge from these sources with data mining algorithms. To update the set of installed algorithms the system does not need a restart, so high availability can be achieved. Data analytic tasks are defined in a  programming language of the Microsoft .NET platform with libraries provided by the system. Thus, experienced users are not limited by graphical designers and their features, and are able to create complex intelligent analytic tasks. For storing and querying results a special storage system is outlined.

English abstract

As the amount of generated and stored data in enterprises increases, the significance of fast analyzing of this data rises. This paper introduces data mining system designed for high performance analyses of very large data sets, and presents its principles. The system supports processing of data stored in relational databases and data warehouses as well as processing of data streams, and discovering knowledge from these sources with data mining algorithms. To update the set of installed algorithms the system does not need a restart, so high availability can be achieved. Data analytic tasks are defined in a  programming language of the Microsoft .NET platform with libraries provided by the system. Thus, experienced users are not limited by graphical designers and their features, and are able to create complex intelligent analytic tasks. For storing and querying results a special storage system is outlined.

Keywords

Data Mining System, Knowledge Discovery, Data Stream, OLAP

RIV year

2012

Released

27.06.2012

Publisher

SciTePress - Science and Technology Publications

Location

Wrocław

ISBN

978-989-8565-13-6

Book

Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2012)

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

187

Pages to

192

Pages count

6

Documents

BibTex


@inproceedings{BUT96956,
  author="Jan {Kupčík} and Tomáš {Hruška}",
  title="Towards Online Data Mining System for Enterprises",
  annote="As the amount of generated and stored data in enterprises increases, the
significance of fast analyzing of this data rises. This paper introduces data
mining system designed for high performance analyses of very large data sets, and
presents its principles. The system supports processing of data stored in
relational databases and data warehouses as well as processing of data streams,
and discovering knowledge from these sources with data mining algorithms. To
update the set of installed algorithms the system does not need a restart, so
high availability can be achieved. Data analytic tasks are defined in a 
programming language of the Microsoft .NET platform with libraries provided by
the system. Thus, experienced users are not limited by graphical designers and
their features, and are able to create complex intelligent analytic tasks. For
storing and querying results a special storage system is outlined.",
  address="SciTePress - Science and Technology Publications",
  booktitle="Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2012)",
  chapter="96956",
  edition="NEUVEDEN",
  howpublished="print",
  institution="SciTePress - Science and Technology Publications",
  year="2012",
  month="june",
  pages="187--192",
  publisher="SciTePress - Science and Technology Publications",
  type="conference paper"
}